长短周期记忆网络微调的氧气浓缩器寿命预测模型迁移

针对寿命预测模型迁移问题,提出了一种长短周期记忆网络微调(long short-term memory fine tune, LSTM-fine-tune)的迁移模型,利用理想条件下的试验数据对模型进行训练。在迁移过程中,对部分LSTM网络层进行冻结,利用实际服役环境下的数据对网络其他部分进行修正。为验证模型的泛化能力,采用不同相位与幅值的正弦函数生成数据,通过学习数据获取正弦函数的经验知识,并应用至其他正弦函数的回归,结果表明LSTM-fine-tune模型能够快速拟合,平均均方误差仅为1.033 5,明显低于直接预测误差1.536 8。为通过实际监测数据检验本方法泛化能力,分别获取了试验条件下与实际服役环境下氧气浓缩器的数据,对模型的泛化能力进行验证。结果表明,迁移后训练集预测精度提高了43.0%,测试集预测精度提高了20.2%。

Transfer of oxygen concentrator life prediction model based on LSTM-fine-tune

To transfer the life prediction model, an LSTM-fine-tune(long short-term memory fine tune) model was proposed. The model was trained by using experimental data under ideal conditions. During the transfer process, part of the LSTM network layer was frozen, and other parts of the network were modified by using data in actual service environment. In order to verify the generalization ability of the model, sinusoidal functions with different phases and amplitudes to generate data were used, obtained the knowledge of the sinusoidal function, and applied it to the regression of other sinusoidal functions. The results show that the LSTM-fine-tune model can be fitted quickly, and the average mean square error is only 1.033 5. It is significantly lower than the direct prediction error 1.536 8. In order to test the generalization ability of this method through actual monitoring data, the data of oxygen concentrators under test conditions and actual service environment respectively is obtained, verifies the generalization ability of the model. The results show that the prediction accuracy of the training set is improved by 43.0% and that of the test set is improved by 20.2%.